No abstract

Molecular classification of bladder cancer is becoming increasingly important for its clinical management. And, the current classifications are primarily based on gene expression profiles. We identified four immunotypes of bladder cancer (referred to as C1-C4) based on gene expression profiles performed by immune-related gene sets in three independent data sets, and proved that this classification is effective and reproducible. We found that C2 is an immune-infiltrating type and C4 is an immune "desert" type. These types are characterized by the up-and downregulation of genes encoding numerous immune checkpoint proteins and HLA and regulating human immune cell subgroups. The survival rate was better for the C2 subtype than for other subtypes. We believe that this can be explained by the antitumor effects of CD4 memory T cells and CD8 T cells as well as their ability to circumvent M0 macrophage antitumor immunity. In addition, C2 was most sensitive to not only anti-PD-1 immunosuppressive therapy, but also conventional chemotherapeutics such as gemcitabine and bleomycin. The C4 subtype was most sensitive to the chemotherapy drugs cisplatin and doxorubicin. This theoretical framework may guide the personalized treatment of bladder cancer in the future. It is worth noting that the C2 immune infiltration type positively correlates with a variety of stromal components, such as enrichment of endothelial cells and fibroblasts, epithelial-mesenchymal transition, and angiogenesis, together with enrichment of seven kinds of stem cells. We further identified tumor-related JAK-STAT and other signaling pathways in the C2 subtype, along with important mutations in the proteins involved in these pathways, revealing the complex mechanism underlying tumor immune escape. Our results, and particularly the identification of hub genes specific to the C2 and C4 subtypes, provide a reference for the development of immunotherapeutic agents against bladder cancer.

Recent cancer studies have found that the netrin family of proteins plays vital roles in the development of some cancers. However, the functions of the many variants of these proteins in cancer remain incompletely understood. In this work, we used the most comprehensive database available, including more than 10000 samples across more than 30 tumor types, to analyze the six members of the netrin family. We performed comprehensive analysis of genetic change and expression of the netrin genes and analyzed epigenetic and pathway relationships, as well as the correlation of expression of these proteins with drug sensitivity. Although the mutation rate of the netrin family is low in pan-cancer, among the tumor patients with netrin mutations, the highest number are Uterine Corpus Endometrial Carcinoma patients, accounting for 13.6% of cases (54 of 397). Interestingly, the highest mutation rate of a netrin family member is 38% for NTNG1 (152 of 397). Netrin proteins may participate in the development of endocrine-related tumors and sex hormone-targeting organ tumors. Additionally, the participation of NTNG1 and NTNG2 in various cancers shows their potential for use as new tumor markers and therapeutic targets. this analysis provides a broad molecular perspective of this protein family and suggests some new directions for the treatment of cancer. Cytokines, growth factors, angiogenic factors, and extracellular proteases are secreted to the outside of the cell to produce biological functions 1. These secreted proteins participate in the immune regulation of chronic inflammation 2,3 and the occurrence of lipid metabolism diseases 4 , but also play important roles in tumor invasion, metastasis, immunity, and drug resistance 5-8. For example, CD317 and EGFR are secreted proteins that are potential diagnostic markers for non-small cell lung cancer 9. CD63 is secreted by melanoma into the blood and can be used as a protein marker 10. HGF inhibits the treatment of RAF inhibitors of BRAF mutant melanoma 11. The netrins are neuroguiding factors with axonal guiding function, and are similar to laminin in structure. Netrins were first discovered in C. elegans in 1990 12 , and this family of proteins includes the secreted proteins Netrin-1 (NTN1), Netrin-3 (NTN3), Netrin-4 (NTN4), and Netrin-5 (NTN5). The secreted proteins have a common domain structure, with an N-terminal laminin repeat (Laminin N-terminal), three cysteine-rich EGF modules (V-1, V-2, and V-3), and a positively charged C-terminal domain (NTR) 13. The netrin family also includes two membrane-binding proteins, Netrin-G1 (NTNG1) and Netrin-G2 (NTNG2) 14. Although these proteins also have Laminin N-terminal and Laminin EGF domains, their ends have different functions due to GPI 15. The major binding receptors of the secreted netrin proteins are DCC and UNC5 homologue family UNC5A-D, which are both dependent receptors. Netrin binding to a receptor promotes cell survival, proliferation, and differentiation, and without netrin binding, the receptor induces apoptosis 16,17....

Background Currently, no molecular classification is established for bladder cancer based on metabolic characteristics. Therefore, we conducted a comprehensive analysis of bladder cancer metabolism-related genes using multiple publicly available datasets and aimed to identify subtypes according to distinctive metabolic characteristics. Methods RNA-sequencing data of The Cancer Genome Atlas were subjected to non-negative matrix fractionation to classify bladder cancer according to metabolism-related gene expression; Gene Expression Omnibus and ArrayExpress datasets were used as validation cohorts. The sensitivity of metabolic types to predicted immunotherapy and chemotherapy was assessed. Kaplan–Meier curves were plotted to assess patient survival. Differentially expressed genes between subtypes were identified using edgeR. The differences among identified subtypes were compared using the Kruskal–Wallis non-parametric test. To better clarify the subtypes of bladder cancer, their relationship with clinical characteristics was examined using the Fisher’s test. We also constructed a risk prediction model using the random survival forest method to analyze right-censored survival data based on key metabolic genes. To identify genes of prognostic significance, univariate Cox regression, lasso analysis, and multivariate regression were performed sequentially. Results Three bladder cancer subtypes were identified according to the expression of metabolism-related genes. The M1 subtype was characterized by high metabolic activity, low immunogenicity, and better prognosis. M2 exhibited moderate metabolic activity, high immunogenicity, and the worst prognosis. M3 was associated with low metabolic activity, low immunogenicity, and poor prognosis. M1 showed the best predicted response to immunotherapy, whereas patients with M1 were predicted to be the least sensitive to cisplatin. By contrast, M2 showed the worst predicted response to immunotherapy but was predicted to be more sensitive to cisplatin, doxorubicin, and other first-line anticancer drugs. M3 was the most sensitive to gemcitabine. The risk model based on metabolic genes effectively predicted the prognosis of bladder cancer patients. Conclusions Metabolic classification of bladder cancer has potential clinical value and therapeutic feasibility by inhibiting the associated pathways. This classification can provide valuable insights for developing precise bladder cancer treatment.